Consumers Attitude towards Online Shopping:...

International Journal of Management Studies ISSN(Print) 2249-0302 ISSN (Online)2231-2528 http://www.researchersworld.com/ijms/

Vol.–V, Issue –3(4), July 2018 [1]

DOI : 10.18843/ijms/v5i3(4)/01

DOIURL :http://dx.doi.org/10.18843/ijms/v5i3(4)/01

Consumers Attitude towards Online Shopping: Factors Influencing

Young Consumers to Shop Online in Dhaka, Bangladesh

Anamika Datta,

Lecturer,

Department of International Business,

University of Dhaka, Dhaka, Bangladesh.

Mithun Kumar Acharjee,*

Assistant Professor of Statistics,

Department of International Business,

University of Dhaka, Dhaka, Bangladesh. *(Corresponding Author)

ABSTRACT

Dhaka, the capital city of Bangladesh is one of the most populated cities around the world and is

progressing towards digital explosion that creates high importance on the evaluation of the

current acceptance level of online shopping by the young people. Thus, understanding the overall

condition of consumer’s attitude towards online shopping is important for Bangladesh. This paper

has focused on identifying the different factors that influence young consumers’ attitude towards

online shopping in Dhaka. This study collects data through a structured questionnaire using

convenience sampling where the study cases are Dhaka University students. To measure the

impact, a multiple regression model has been considered. Result shows that, all eight factors have

a positive impact on young consumer’s attitude towards online shopping: security (beta value:

0.160), after sales service (0.062), time savings (0.191), return policy (0.170), website design

(0.183), product quality (.053), previous experience (.084) and reputation of the online vendor

(.197). Analysis of Variance shows that, six major socio-demographic factors: gender, family

income, personal income, educational level, member of the current residence and daily use of

internet has statistically significant association (p value < 0.05) with consumer’s attitude towards

online shopping. T-test shows that, in terms of convenience and existence of return policy,

consumers prefer shopping through online. But when it comes to fun, bargaining and roaming

traditional shopping is still preferable. For the betterment of this scenario, this paper recommends

to build trust among consumers, providing security, return policy, creating awareness and

promoting this new form of business.

Keywords: Online shopping, Traditional shopping, Digital explosion, E-Commerce.

INTRODUCTION:

The modern time of human civilization is experiencing its true phenomenon through the heavy explosion of

ICT which turns McLuhan‟s Global Village into a reality. From 2000 to 2017, the growth rate of internet user is

around 976.4% (Internet World Stats, 2017). Within the last five years, number of internet users rises from

2,405 million (June, 2012) to 3,885 million (June, 2017) that accounts for 51.7% of the world population

(Internet World Stats, 2017). Technological progress, infrastructure development and falling prices of mobile

phones are among the most important factors that have contributed to this unexpected growth (ICT Facts and

Figure, ITU, 2015).

Combining the opportunity of this massive usage of internet and the exponential growth of business, e-

commerce has become a new rewarding form of business. With the first sale of Sting album, „Ten Summoner‟s

Tales‟ in 1994, internet started to be a new place for business. After that, Amazon, in 1995, has expanded this

platform for both Business to Business (B2B) and Business to Consumer (B2C) online shopping (Parker-Hall,

http://dx.doi.org/10.18843/ijms/v5i3(4)/01


Vol.–V, Issue –3(4), July 2018 [2]

2009). At present, online shopping is said to be the third most popular activity on the internet after email using

and web browsing (UCLA communication policy, 2001).

In 2016, global online shopping was of $1.9 trillion and projection has shown a growth of up to $4.06 trillion by

2020 (statista). According to the United Nations Conference on Trade and Development, online consumers will

grow to 1.623 billion in 2018 from 1.079 billion in 2013. Other than the expansion of internet, the rise in smart

phone penetration rate, mobile phone internet, trust, easier payment system (Neilson, 2017) all has contributed

to this growth.

However, though online shopping behavior has become a common trend of shopping among developed

countries (Particularly European and North American countries), developing world is still lacking behind.

UTCTAD has found that in 2013, digital buyers represented less than 15 percent of the population in Asia and

Oceania, as compared with 60 percent in North America and 49 percent in Western Europe. However, this

scenario is expected to change by near future. In 2017, China has achieved the highest online shopping

penetration rate (statista).

Nevertheless, Bangladesh is also progressing towards having a digitally connected nation which ultimately

helps nationals to become more prone to internet usage. Google research paper titled “Research Insights:

Emerging Trends as Bangladesh Goes Digital” found that people spend Tk 7,594.10 annually for online

shopping (Azad, 2017). According to e-cab, more than 700 e-commerce websites are selling their products on

their website or on Facebook page and this number continues to grow (expo.gov, 2017). But only 23% of the

internet users which is only 2% of the total population shop online (B2C UNCTAD eCommerce Index-2016).

Besides, contribution of eCommerce to real GDP is less than 1 percent. However, with the internet penetration

38.5% in (BTRC, 2016), the market for online shopping grew by 15% to 20% in 2014 (Jahangir Shah, Prothom

Alo, 2014). Thus, with proper influence internet users can be turned into potential online shoppers.

Thus, as Bangladesh is economically progressing and emphasizing more on digital connectivity, it indicates a

great potentiality in this form of business. In order to gain competitive edge in the market, marketers need to

know the consumer behavior in the field of online shopping. So it has become crucial to analyze and identify

the factors which influence consumers to shop online in order to capture their demand. The benefits of this

research work are manifold. First, this study investigates different factors that influence young consumers‟

attitude towards online shopping in Dhaka. Second, examine whether demographic profiles of young

consumers influence their attitude towards online shopping in the central part of Bangladesh. Last and

importantly, this work find out the preference level of the young consumers when it comes to „traditional

shopping‟ and „online shopping‟.

LITERATURE REVIEW:

Electronic commerce is the sharing of business information, maintaining business relationships, and conducting

business transactions by means of telecommunications networks. (Mostaghel, 2006). Among the two categories

of ecommerce, business to business e-commerce, identified by Gröblinghoff (2002), is electronic system

through which companies are doing transactions and sharing information before and the service after

transactions, with their customers and it is offering many number of applications for creating and achieving

easier connections with distributors, resellers, suppliers and etc. Khiabani (2006) defines business to business as

something that includes flow of goods from seller to manufacturers and retailers. B2B communication

symbolizes the larger segment of entire business activity.

On the other hand, business to consumer e-commerce, according to Khiabani (2006) relates to any business

which is offering goods and services to public, in this case consumers, over the internet for their personal usage.

Based on his study simplified definition of B2C e-commerce is the transmission of goods or services from the

seller to the end consumer.

In both the cases, internet users can be internet shoppers or internet browsers (Forsythe and Shi, 2003). Internet

shoppers are the people who shop online whereas internet browsers are the people who just browse the internet

other than shopping purpose. As Forsythe and Shi explain “Internet shopping has become the fastest growing

use of the Internet; most online consumers, however, use information gathered online to make purchases off-

line”. This finding, thus, encourages understanding consumers‟ attitude that influence them to make final

purchasing behavior on virtual platform.

An attitude, according to Solomon (2009), is defined as a general evaluation of a product or service formed

over time. Consumer attitude is composed of beliefs about, feelings about and behavioral intentions toward

some object – within the context of marketing, usually a brand or retail store (USC Marshall). In an alignment,

Solomon (2009) accentuated a relationship between knowing, feeling and doing, largely known as ABC (affect,


Vol.–V, Issue –3(4), July 2018 [3]

behavior and cognition) model of attitude. As with this model, an individual‟s emotion or opinion regarding an

object (affect) together with beliefs, thoughts and attributes associated with it (cognition) may result into a

particular response (behavior).

However, many theories have been developed regarding consumers‟ attitude and buying behavior. Among

them, the classic consumer purchasing decision-making theory can be characterized as a continuum extending

from routine problem-solving behaviors, through to limited problem-solving behaviors and then towards

extensive problem-solving behaviors (Schiffman et al., 2001). The traditional framework to analyze the buyer‟s

decision process is a five-step model (Kotler, 2012). Given the model, the consumer progresses firstly from a

state of felt deprivation (problem recognition), to the search for information on problem solutions. The

information gathered provides the basis for the evaluation of alternatives. The development and comparison of

purchasing evaluation criteria result in the actual decision to buy. Finally, post-purchase behavior is critical in

the marketing perspective, as it eventually affects consumers‟ perception of satisfaction or dissatisfaction with

the product or service. This classic five stage model comprises the essence of consumer behavior under most

contexts (Shergill and Chen, 2005). Thus, consumers' attitude towards online shopping is an important factor

affecting actual buying behavior of the internet user.

Jarvenpaa and Todd (1997) proposed a model of attitudes and shopping intention towards Internet shopping in

general. The model included several indicators, belonging to four major categories; the value of the product, the

shopping experience, the quality of service offered by the website and the risk perceptions of Internet retail

shopping. Later on in 2000, Jarvenpaa et al. tested a model of consumer attitude towards specific web base

stores, in which perceptions of the store's reputation and size were assumed to affect consumer trust of the

retailer. On the same time, Vellido et al. (2000) found risk perception of users to be the main discriminator

between people buying online and people not buying online. Brynjolfsson and Smith (2000) also pointed out

that branding and trust remain important sources of heterogeneity among Internet retailers.

Besides risk perception, Robinson, Riley, Rettie and Wilsonz (2007), Bhatnagar and Ghose (2004),

Swaminathan (2004), Morganosky and Cude‟s (2000), Darian (1987) claim that the major motivation for

online purchasing is convince in terms of shop at any time, having bundles of items delivered at door step, less

time consuming, flexible, very less physical effort is needed.

Although Corbett (2001) found online shopping time consuming, Rohm and Swaminathan‟s (2004) and Morganosky

and Cude (2000) found it to eliminate traveling time and hassels required to go to the traditional store.

A study by Kamariah and Salwani (2005) shows higher website quality influence consumers to have a positive

attitude towards online shopping. Website design, website reliability or fulfillment, website customer service

and website security or privacy are the most attractive features which influence the perception of the consumer

of online buying (Shergill & Chen , 2005, Liang and Lai , 2000, Reibstein, 2001, Zhang, Dran, Small, and

Barcellos 1999).

In terms of demographic features, studies have shown that online shoppers mainly consist of people with higher

education and income and working in middle to senior management or professionals (Kehoe et al., 1998;

Hoffman et al., 1996). In another study it was found that cyberspace is the domain of young people Bhatnagar

and Ghose (2004). Sim and Koi, (2002) states as main discriminating factors appeared to be gender, income and

educational level.

Online Shopping in Bangladesh:

After prolonged sufferings, Bangladesh is observing phenomenal growths in e-commerce and related

activities on commercial fronts as with the changing mind sets and advent of technologies (Payza, 2015).

They have also added that government in recent past have initiated mane policies and schemes to establish

modern IT infrastructure and internet framework along with technological advancement in

telecommunication including 2G or 3G network for smart handled devices. Among many initiatives, in 2009,

the Bangladesh Bank began permitting online transactions and it also permitted the purchase and sale of

goods and services online using international credit cards in 2013 (export.gov, 2017). With this enormous

effort from government and many non-government institutions, the current volume of online shopping has

become more than Tk. 3.5 billion as of 2014, according to E-commerce industry experts,. This figure rises

further during festivals such as Eid- ul Fitr, while Facebook based commercial activities alone account for

more than 60% of all online shopping during Eid.

Many studies have been conducted o understand whether Bangladeshis has accepted this new form of business

or traditional shopping is still their first preference. This study, thus, tries to find out the underlying factors that


Vol.–V, Issue –3(4), July 2018 [4]

have contributed to the growth of online shopping in Bangladesh and also the causes that makes them skeptic to

buy from virtual market.

METHODOLOGY:

Database:

This study is exploratory by nature. It has used quantitative approach for gathering data. Field survey was the

main technique. A structured questionnaire was developed and used for the survey. However, young generation

of a country is more inclined to the modern technology, capturing only the Gen Y‟s attitude towards online

shopping makes this study more specific and definite. Thus, students from University of Dhaka, the largest

public university in Bangladesh, are used as the sampling elements. In this study, non-probability sampling that

is convenience sampling is used for collecting the information from the respondents. As in previous studies on

online shopping, sample size of 100 to 500 has been determined (Umar and Nasir, 2011; Jusoh and Ling, 2012;

Suhan, 2015), a sample size of 166 is selected.

Variables, statistical techniques and study framework:

Multiple regression analysis is conducted on one dependent variable which is consumer attitude towards online

shopping and eight independent variables are used (Figure 1). These eight factors are: reputation of the online

vendor, perceived security, after sale service, convenience (in term of comparison, 24/7 availability,

information availability), time saving, website design, ICT usage (in terms of expenses for internet per month,

usages of application, hours spent on internet everyday), online shopping experience and product quality (Table

1). Analysis of Variance (ANOVA) (except Gender for which Pearson Correlation test) is done on dependent

variable young consumers‟ attitude towards online shopping and six demographic factors of this group such as:

gender, family income, personal income, education level, member of present residence and internet usage

(Figure 1). To examine the preference level of consumers between online and traditional shopping, five

determinants of shopping are used. Based on these determinants, consumers‟ preferences will be tested through

one sample t-test. These five determinants are: convenience, fun, return policy of the product, existence of

bargaining power and the options to roam around while shopping.

Figure 1: Attitude toward Online Shopping Model

Security

After sales service

Convenience

Reputation of online

vendor

Online shopping

experience

Time saving

Website design

Product quality

Young Consumers’ Attitude

towards Online Shopping

(Dependent Variable)

Gender

Family Income

Personal Income

Educational Level

Member of

Residence

Infl

uen

tia

l fa

cto

rs (

Ind

epen

den

t V

ari

ab

les)

Dem

og

rap

hic fa

ctors (In

dep

end

ent V

aria

bles) Daily usage of

internet


Vol.–V, Issue –3(4), July 2018 [5]

Table 1: Measurement Indicators of the Selected Independent Variables

Independent Variables Measurement Indicators

Security

Credit card fraud

Exposure of sensitive information

Non delivery risk

After sale service

Guarantee or warranty

Returns or replacement

Assistance in maintenance or installation

Convenience

Availability of the detailed information

Available for 24/7

Easy to make comparison

Reputation of online vendor Positive review of experienced customer

Positive comments from known reviewer

Time saving Less time to purchase

No wastage of time

Website design

Easier to search

Safety and ease of navigation and order

Layout helps to choose the right product

Existence of quality information

Online shopping experience Previous experience meets the expectation

Product quality

Images of product are same in reality in terms of color, size

and design.

Materials are used as identified and expected

For evaluating the relationship of specified ten variables on consumers‟ attitude towards online shopping, each

factor is tested based on 5 point Likert scale ranging from 1 (strongly disagree), 3 (Uncertain or not applicable)

to 5 (strongly agree). After that, multiple regression analysis is conducted for investigating the most influential

factor. For assessing the characteristics of young online shoppers, one way ANOVA (except Gender for which

Pearson Correlation test) is conducted for each identified factors. Finally, one-sample t-test is conducted to

judge the preference of shopping of young consumers between online and traditional. Each of these methods

has previously been used by many authors for assessing the relationship among variables and judging

consumers‟ preferences which shows the generalizability and acceptability these methods.

HYPOTHESIS:

This study tests the following hypothesis:

H1= There is a statistically significant relationship between gender and attitude of online shopping.

H2= There is a statistically significant relationship between family income and attitude of online shopping.

H3= There is a statistically significant relationship between personal income and attitude of online shopping.

H4= There is a statistically significant relationship between educational level and attitude of online shopping.

H5= There is a statistically significant relationship between place of residence and attitude of online shopping.

H6= There is a statistically significant relationship between daily internet usage and attitude of online shopping.

FINDINGS AND DISCUSSION:

Table 2: Frequency distribution of the respondents and their socio-demographic characteristics

Variables Category Frequency Percentage

Gender Male

Female

84

82

50.6

49.4

Family Income

Less than tk 15000

tk 15000 to tk 25000

tk 25000 to tk 40000

More than tk 40000

26

47

36

57

15.7

28.3

21.7

34.3


Vol.–V, Issue –3(4), July 2018 [6]

Variables Category Frequency Percentage

Personal Income

Less than tk 3000

tk 3000 to tk 6000

tk 6000 to tk 10000

More than tk 10000

73

46

32

15

44

27.7

19.3

9

Educational Level

Undergraduate

Graduate

Postgraduate

74

60

32

44.6

36.1

19.3

Member of the current

residence

With family

With relatives

With friends or alike

Others

83

8

45

30

50

4.8

27.1

18.1

Daily usage of internet

1 hour or below

1 to 3 hour

3 to 5 hour

5 to 7 hour

More than 7 hour

42

58

37

11

18

25.3

34.9

22.3

6.6

10.8

Table 2 shows that among the 166 young respondents 50.6 percent of them are male whereas 49.4 percent of

them are female. 15.7 percent of the respondents‟ family income is less than tk.15000. But most (34.4 percent)

respondent‟s family income is more than tk. 40000. 47 and 36 of the consumers fall in the second and third

category respectively. Moreover, in terms of personal income, 44 percent of the respondents fall in the category

of „less than tk 3000‟. That indicates 73 respondents earn less than tk 3000. Whereas, 27.7 percent of them earn

tk 3000 to tk 6000 and 19.3 percent earns t 6000 to tk 10000. But only 9 percent of them earn more than tk

10000. Most of the respondents (44.6 percent) are the undergraduate students. Whereas 36.1 percent are

graduate and 19.3 percent are postgraduate. Besides, among the respondents most of them live with their family

(50 percent) whereas only 8 percent of them live with their relatives. As data is collected solely from Dhaka

University, it was asked whether they live with friends or alike i.e. whether they are residing in any hall. 27.1

percent respondents fall in this category. Lastly, most respondents (34.9 percent) use internet 1 to 3 hours

whereas 25.3 percent use internet less than 1 hour daily. Whereas, 22.3 percent of them fall in the 3 to 5

category, 6.6 percent use internet 5 to 7 hours and 10.8 percent use it for more than 7 hours.

Table 3: Frequency distribution of the selected variables to measure consumers’

attitude towards online shopping

Variables Measurement

indicators Category Frequency Percentage

Security

Credit card fault

Strongly agree

Agree

Uncertain or not applicable

Disagree

Strongly Disagree

43

76

21

18

8

25.9

45.8

12.7

10.8

4.8

Exposure of sensitive

information

Strongly agree

Agree


Disagree

Strongly Disagree

74

70

4

15

3

44.6

42.2

2.4

9.0

1.8

Non delivery risk

Strongly agree

Agree


Disagree

Strongly Disagree

29

51

19

60

7

17.5

30.7

11.4

36.1

4.2


Vol.–V, Issue –3(4), July 2018 [7]



After sales

service

Guarantee

Strongly agree

Agree


Disagree

Strongly Disagree

6

44

36

57

23

3.6

26.5

21.7

34.3

13.9

Returns or replacement

Strongly agree

Agree


Disagree

Strongly Disagree

15

38

18

78

17

9.0

22.9

10.8

47.0

10.2

Assistance in

maintenance or

installation

Strongly agree

Agree


Disagree

Strongly Disagree

17

43

31

62

13

10.2

25.9

18.7

37.3

7.8

Convenience

Availability of detailed

information

Strongly agree

Agree


Disagree

Strongly Disagree

25

59

21

51

10

15.1

35.5

12.7

30.7

6.0

Available for 24/7

Strongly agree

Agree


Disagree

Strongly Disagree

44

78

11

23

10

26.5

47.0

6.6

13.9

6.0

Easy to make

comparison

Strongly agree

Agree


Disagree

Strongly Disagree

50

84

13

11

8

30.1

50.6

7.8

6.6

4.8

Reputation

of online

vendor

Positive review of

experienced customer

Strongly agree

Agree


Disagree

Strongly Disagree

60

81

11

13

1

36.1

48.8

6.6

7.8

0.6

Positive comments from

known reviewer

Strongly agree

Agree


Disagree

Strongly Disagree

63

82

14

4

3

38.0

49.4

8.4

2.4

1.8

Time saving

Less time to purchase

Strongly agree

Agree


Disagree

Strongly Disagree

44

91

18

10

3

26.5

54.8

10.8

6.0

1.8

No wastage of time

Strongly agree

Agree


Disagree

Strongly Disagree

52

75

14

19

6

31.3

45.2

8.4

11.4

3.6


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Website

design

Easier to research

Strongly agree

Agree


Disagree

Strongly Disagree

28

89

13

33

3

16.9

53.6

7.8

19.9

1.8

Safety and ease of

navigation

Strongly agree

Agree


Disagree

Strongly Disagree

58

71

14

21

2

34.9

42.8

8.4

12.7

1.2

Layout helps to choose

the right product

Strongly agree

Agree


Disagree

Strongly Disagree

20

85

18

38

5

12.0

51.2

10.8

22.9

3.0

Existence of quality

information

Strongly agree

Agree


Disagree

Strongly Disagree

57

73

10

20

6

34.3

44.0

6.0

12.0

3.6

Online

shopping

experience

Previous experience

meets the expectation

Strongly agree

Agree


Disagree

Strongly Disagree

57

82

14

11

2

34.3

49.4

8.4

6.6

1.2

Product

quality

Images of product are

same in reality

Strongly agree

Agree


Disagree

Strongly Disagree

7

27

20

86

26

4.2

16.3

12.0

51.8

15.7

Materials are used as

identified and expected

Strongly agree

Agree


Disagree

Strongly Disagree

3

31

20

81

31

1.8

18.7

12.0

48.8

18.7

Table 3 shows that for the security issues, 45.8 percent of them agree that they are sacred of the chances of the

credit card fault. Whereas, 25.9 percent of them „strongly agree‟ regarding this issue. On the other hand, around

15 percent of the consumers disagree or strongly disagree that they are not scared of the credit card default.

However, 12.7 percent of them are neutral on this issue. Moreover, around 87 percent of them agree or strongly

agree that online shopping may expose their sensitive information. Only around 10 percent of the respondents

disagree and strongly disagree regarding this matter. In case of delivery risk, 36.1 percent of the consumers

believe that online shopping has this risk. But around 48 percent of them agree and strongly agree that this

exists. Only 11.4 percent of them remain neutral on this issue.

Another selected variable, after sales service, around 34.3 percent of the respondents believe that online

shopping provide any guarantee or warranty. Around 48 percent of them disagree or strongly disagree on this

matter whereas 30 percent of them agree or strongly agree that online shopping provide guarantee and warranty.

They may have previous experience or have seen or heard that online shopping provide guarantee and warranty.

In case or return or replacement, most of the respondents (47 percent) disagree that online shopping provide any

return or replacement policy. But 31 percent of them have a reverse opinion. In case of maintenance and install

after sales, 37.3 percent disagree of whether they provide any of these services. Whereas, 25.9 percent believe

that they do. For the third variable, convenience, 35.5 percent believe that detailed information is avail in the

online shop‟s website. Whereas, 30.7 percent of them disagree with it. Whether online shopping is available for


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24/7, 47 percent response in affirmative, 26.5 percent strongly agree on this. On the other hand, 20 percent of

them refuse its anytime availability. In case of comparison, 50.6 of the respondents agree that they can easily

compare products online whereas, 30.1 of them agree with it strongly. Whereas, around 10 percent of them

response in negative.

Besides, for the variable of reputation of the online vendor, 48.8 percent of them agree that positive review of

the previous customers‟ influence them to purchase and 36.1 of the customers agree strongly on it. Whereas

around 8 percent disagree or strongly agree on experienced customers review matters in anyway. However, 49.4

percent of the respondents agree that positive comments from known reviewer matters whereas 38 percent

strongly believe it. However, only around 4 percent of the customers disagree or strongly disagree with this

matter. In case of saving time, 54.8 percent of them agree that online shopping tae less time to purchase and

26.5 percent strongly agree on it. Whereas, 11.4 percent disagree on this issue. 53.6 percent of the respondents

agree that it is easier to search products online whereas 19.9 percent of them disagree on it. 42.8 percent of the

respondents agree that it is safe and easy to navigate but only 12.7 percent disagree with it. 51.2 percent says

online shops website‟s layout helps them to choose the right product but 22.9 percent disagree with it. 44

percent of the respondents agree that quality information of the online shops website is necessary but 12.0

percent of them disagree. In case of whether consumers believe that their previous purchasing experience from

online influence them, 49.4 percent and 34.3 percent agree and strongly agree with it. Whereas, among the

respondents only 6.6 percent of them disagree with it. In case of product quality, only around 21 percent of the

respondents agree and strongly agree that products are as expected but around 67 percent of the respondents

remains disagree and strongly disagree on this matter. Whereas in case of materials, responses are almost alike.

Around 67 percent believe materials of the products are not what it is said before purchasing but 20 percent of

them agree and strongly agree on this.

Consumers’ attitude towards online shopping in Dhaka by socio-demographic variables:

Table 4: ANOVA table for consumers’ attitude towards online shopping with respondent’s family income

Sum of Squares df Mean Square F P Value

Between Groups 1.911 3 .637

1.964

.012 Within Groups 52.545 162 .324

Total 54.456 165 …………….

Table 4 shows that P value is less than 0.05 which suggests that significant relationship exists between

consumers‟ attitude towards online shopping and respondent‟s family income. Here, null hypothesis is rejected.

Table 5: ANOVA table for consumers’ attitude towards online shopping with respondent’s personal income

Sum of Squares Df Mean Square F P Value

Between Groups .192 3 0.064

1.91

.002 Within Groups 54.264 162 .335

Total 54.456 165 …………….

Table 5 depicts the presence of significant statistical relationship between consumers‟ attitude towards online

shopping and personal income, i.e. null hypothesis rejected.

Table 6: ANOVA table for consumers’ attitude towards online shopping with respondent’s educational level


Between Groups .439 2 0.220

.663

.045 Within Groups 54.017 163 .331

Total 54.456 165 …………….


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Table 6 shows that the above null hypothesis is rejected i.e. there exists significant relationship between

educational level and consumers‟ attitude towards online shopping.

Table 7: ANOVA table for consumers’ attitude towards online shopping with respondent’s place of residence


Between Groups 5.936 3 1.979

6.606

.000 Within Groups 48.520 162 .300

Total 54.456 165 …………….

From Table 7 it is clear that p value is less than 5 percent level of significance which shows null hypothesis is

rejected, that is, there exists a significant relationship between these two variables.

Table 8: ANOVA table for consumers’ attitude towards online shopping with

respondent’s daily usage of internet


Between Groups 1.096 4 .274

.826

.005

Within Groups 53.361 161 .331

Total 54.456 165 …………….

Table 8 shows the existence of relationship between these variables as p value is lower than level of

significance rejecting null hypothesis.

Preference between Traditional Shopping and Online Shopping:

Based on 5 percent level of significance, consumers‟ preference between traditional and online shopping is

analyzed using five variables such as: convenient, fun, return policy, bargaining and roaming. Consumers have

been asked to express their preferences in terms of these five variables. As the dependent variable is a

continuous variable and is approximately normally distributed, one sample t test shows the following result:

Table 9: One Sample t Test

Test Value = 3

T Degrees of

freedom P value

Mean

Difference

95% Confidence

Interval of the

Difference

Lower Upper

Convenient -6.112 165 .000 -.52410 -.6934 -.3548

Fun -2.474 165 .014 -.50602 -.9098 -.1022

Return Policy -14.429 165 .000 -.95783 -1.0889 -.8268

Bargaining -4.796 165 .000 -.40361 -.5698 -.2375

Roaming -5.305 165 .000 -.49398 -.6778 -.3101

Table 9 shows that all the p value is lower than 5 percent level of significance. It indicates that in terms of

convenient, consumers prefer to shop online. That they have a firm believe that online shopping is available

anywhere at any time. On the other hand, when they have been asked whether they find traditional shopping

more fun than online shopping, their responses generates lower p value than 0.05 which indicates consumers

still prefer traditional shopping as may be they can spend time with their closest one while shopping, can spend

some time happily or may check the product physically.

However, in terms of return policy, most of the consumers believe they may choose online shopping if they

have this option available. In real, very few online shops in Bangladesh provide return policy which means

people will not be able to return or replace a product if they cannot like it after getting it in real. This is one of

the reason for which many consumers would like to shop online if it becomes available as the p value which is

lower than 0.05 indicates.


Vol.–V, Issue –3(4), July 2018 [11]

However, when it comes to bargaining, p value again is lower than 0.05 which indicates that consumers do not

like online shopping as they cannot bargain. Almost every online shop does not have the option for bargaining,

as a result, people may become skeptical regarding the prices and do not buy from online at all.

Lastly, in terms of roaming, young consumers like to visit the shopping complex and purchase rather than

sitting in front of the computer. That is why p value for this variable is lower than 0.05. As in the case of two

complementary variables, fun and roaming, results shows than same i.e. young consumers prefer traditional

shopping than online shopping.

As the above result shows young consumers in Dhaka city still prefer traditional shopping in terms of fun,

bargaining and roaming. But when it comes to convenience and existence of return policy they like to go for

online shopping.

Factors influencing young consumers to shop online:

As the dependent variable, i.e. consumers‟ attitude towards online shopping is continuous variable and different

selected independent variables their relationship needs to be tested with the dependent variables that are why,

multiple regression models has been chosen. Using eight independent variables and a continuous dependent

variable, the multiple regression equation is as follow:

Y= β0 + β1 X1 + + β2 X2 ………. + β8 X8

Where, Y is the consumers‟ attitude towards online shopping, β0 is the constant or Intercept, β1 to β6 is the

regression coefficients, X1 is security, X2 is after sales service, X3 is convenience, X4 indicates reputation of the

online vendor, X5 is denoted as time saving, X6 indicates website design, X7 is online shopping experience and

X8 represents product quality.

Table 10: Variables in the multiple regression model

Model Unstandardized Coefficients

Standardized

Coefficients T Sig.

B Std. Error Beta

1 (Constant) -.259 .127 -2.044 .043

Security .160 .025 .240 6.487 .000

After sales Services .062 .021 .110 2.899 .004

Convenience .170 .023 .294 7.281 .000

Reputation .197 .033 .248 6.031 .000

Time saving .191 .026 .278 7.229 .000

Website Design .183 .026 .284 7.103 .000

Product quality .053 .026 .077 2.061 .041

Previous experience .084 .023 .130 3.566 .000

Table 10 begets the following equation:

Y= -0.259 + 0.160 (security) + 0.062 (after sales service) + 0.170 (convenience) + 0.197 (reputation) + 0.191

(time saving) + 0.183 (website design) + 0.053 (product quality) + 0.084 (previous experience).

Here, p values of all the independent variable are lower than 0.05 which suggests that all the independent

variables are statistically significant. That is, all the variables have significant impact on consumer‟s attitude

towards online shopping.

However, the coefficient suggests, when 1 unit increase in security, consumers‟ attitude towards online

shopping increases 0.160 times. When „after sales service‟ increases by 1 unit, attitude towards online shopping

increases 0.062 times. 1 unit increase in convenience, consumers‟ attitude will increase 0.170 times. This

attitude of the consumers will be increased by 0.197 times with 1 unit change in reputation. Similarly,

consumers‟ attitude towards online shopping will be increased 0.191 if website design is increased by 1 unit.

Not only that the last two independent variables, product quality and previous experience significantly influence

consumers‟ attitude in a way where 1 unit change in their value, attitude increase 0.053 and 0.084 respectively.

CONCLUSION:

As the economy of Bangladesh grows, new forms of business can raise customer retention rate. Online

shopping provides consumers a virtual platform where buyers and sellers can interact at anytime regardless of


Vol.–V, Issue –3(4), July 2018 [12]

the place. This study thus tries to focus on if people are accepting online shopping in Bangladesh as a new form

of business and underlying factors that shape their attitude to shop online. Study finds out that six socio-

demographic factors: gender, family income, personal income, educational level, place of residence and daily

internet usage are significantly correlated or have influence on young consumers‟ attitude towards online

shopping in Dhaka. Moreover, each of the eight variables has a significant correlation with consumers‟ attitude

towards online shopping. Result shows that 1 unit increases in security, consumers‟ attitude towards online

shopping increases 0.160 times. When „after sales service‟ increases by 1 unit, attitude towards online shopping

increases 0.062 times. 1 unit increase in convenience, consumers‟ attitude will increase 0.170 times. This

attitude of the consumers will be increased by 0.197 times with 1 unit change in reputation. Similarly,

consumers‟ attitude towards online shopping will be increased 0.191 if website design is increased by 1 unit.

Not only that the last two independent variables, product quality and previous experience significantly influence

consumers‟ attitude in a way where 1 unit change in their value, attitude increase 0.053 and 0.084 respectively.

However, in case of convenience and existence of return policy consumers prefer online shopping. But in case

of fun, bargaining and roaming consumers prefer to go for shopping complex. Young consumers in Dhaka city

still refer traditional shopping than online shopping. Bangladesh is an emerging developing nation. Like many

other developing nations, online shopping has been on its hike here. And if the business manager provide the

chances for bargaining, return policy, make this shopping more fun and go for „click and mortar‟, then this

preference level may become more positive towards online shopping in near future. Finally, maximization of

the quality, innovation and customer relationship has to be ensured to build trust among young consumers.

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